Test apparatus for performing a test on a device under test and data set filter for filtering a data set to obtain a best setting of a device under test

ABSTRACT

A test apparatus for performing a test on a device under test includes a data storage unit being configured to store sets of input data applied to the device under test during the test and to store the respective output data of the device under test, the output data being obtained from the device under test as a response to the input data including values of setting variables related to settings of the device under test and values of input variables including further information, each set of input data representing one test case; and a data processor configured to process the data stored in the data storage unit such that a best combination of setting variables of the device under test is determined for one or more combinations of the input variables to obtain an optimized setting of the device under test for the one or more combinations of the input variables.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of copending InternationalApplication No. PCT/EP2017/055371, filed Mar. 7, 2017, which isincorporated herein by reference in its entirety.

BACKGROUND OF THE INVENTION

The present invention relates to a test apparatus for performing a teston a device under test and a data set filter for filtering a data set toobtain a best setting of the device under test. The data set filter maybe part of the device under test. According to embodiments, anoptimization in a test case database is shown, wherein the test casedatabase or a data storage unit may comprise multiple sets of input dataand respective output data.

Calibration/correction & FW (firmware) optimization shall find the bestdevice settings (y) as a function of known information (x). This is amultivariate optimization problem. The usual method changes yiteratively, and measures the optimization target (g) for all values ofx in each iteration. Known optimization algorithms operate onalgorithmic representations of functions that shall be optimized.

Therefore, classification algorithms may calculate a function of bestsetting variables dependent on the input of known information (x).Deriving good results from these classification or regression algorithmsinvolves a deep knowledge of the used algorithm to find the optimalresult and not to remain in a local minimum only representing anon-optimal result. Moreover, these algorithms are computationallycomplex and therefore very time consuming.

SUMMARY

According to an embodiment, a test apparatus for performing a test on adevice under test may have: a data storage unit configured to store setsof input data applied to the device under test during the test and tostore the respective output data of the device under test, wherein theoutput data is acquired from the device under test as a response of thedevice under test to the input data, wherein the input data has valuesof setting variables related to settings of the device under test andvalues of input variables having further information, wherein each setof input data represents one test case; and a data processor configuredto process the data stored in the data storage unit such that a bestcombination of setting variables of the device under test is determinedfor one or more combinations of the input variables to acquire anoptimized setting of the device under test for the one or morecombinations of the input variables.

Another embodiment may have a data set filter for filtering a data setto acquire a best setting of a device under test for current inputvariables using an optimization vector, the optimization vectorrepresenting a relation between an index of input variables and an indexof a combination of setting variables indicating the best setting of thedevice under test.

According to another embodiment, a method for performing a test on adevice under test may have the steps of: storing sets of input dataapplied to the device under test during the test; storing the respectiveoutput data of the device under test derived from the device under testas a response of the device under test to the input data, wherein theinput data has values of variables related to settings of the deviceunder test and values of input variables having known information,wherein each set of input data represents one test case; and processingthe data stored in the data storage unit such that the best setting ofthe device under test for one or more combinations of the furthervariables is determined.

According to another embodiment, a method for filtering a data set toacquire a best setting of the device under test for current inputvariables using an optimization vector may have the step of: forming theoptimization vector representing a relation between an index of inputvariables and an index of a combination of setting variables indicatingthe best setting of the device under test.

Another embodiment may have a non-transitory digital storage mediumhaving a computer program stored thereon to perform the method forperforming a test on a device under test, the method having the stepsof: storing sets of input data applied to the device under test duringthe test; storing the respective output data of the device under testderived from the device under test as a response of the device undertest to the input data, wherein the input data comprises values ofvariables related to settings of the device under test and values ofinput variables having known information, wherein each set of input datarepresents one test case; and processing the data stored in the datastorage unit such that the best setting of the device under test for oneor more combinations of the further variables is determined, when saidcomputer program is run by a computer.

Another embodiment may have a non-transitory digital storage mediumhaving a computer program stored thereon to perform the method forfiltering a data set to acquire a best setting of the device under testfor current input variables using an optimization vector, the methodhaving the step of: forming the optimization vector representing arelation between an index of input variables and an index of acombination of setting variables indicating the best setting of thedevice under test, when said computer program is run by a computer.

Embodiments of the present invention show a test apparatus forperforming a test on a device under test. The test apparatus comprises adata storage unit and a data processor. The data storage unit isconfigured to store sets of input data applied to the device under testduring the test and to store the respective output data of the deviceunder test. The output data may be obtained from the device under testas a response of the device under test to the input data, wherein theinput data comprises values of setting variables related to settings ofthe device under test and values of input variables comprising furtherinformation. Each set of input data represents one test case. Moreover,the data processor is configured to process the data stored in the datastorage unit such that a best combination of setting variables of thedevice under test is determined for one or more combinations of theinput variables to obtain an optimized setting of the device under testfor the one or more combinations of the input variables.

The present invention is based on the finding that it is advantageous toderive the optimal settings of the device under test directly from thedatabase, i.e. the data storage unit, by omitting the cumbersome step ofderiving a function representing the setting parameters of the deviceunder test based on current input data. This speeds up the totaloptimization process and is furthermore less error prone. The proposalhere is to create and store a large number of test cases in a test casedatabase, where the optimization target has been measured for manydifferent values of x and y. This database is then analyzed to find theoptimal function y(x) and to verify the optimization result.

Therefore, if not already present, a countable number of possible valuesof input variables and a countable number of possible variable values ofsetting variables is determined, for example, by quantizing input and/orsetting variables having too many potential values when operating thedevice under test. This may be real or integer variables, etc. Moreover,according to embodiments, the data storage unit is transformed into anew (further, second) database (table, matrix, list) where all possiblecombinations of input variables are applied or located along a firstdimension of the database and all possible combinations of settingvariables are located along a second dimension of the further database.Entries of the further database may be an index of the current test caseto link the respective input variables and setting variables of a commontest case and furthermore to link the original or non-quantized data ofthe database or the data storage unit to the quantized values of thefurther database.

According to further embodiments, the indices of test cases within theentries or fields of the further database are weighted based on theoptimization target (g). Each input of the non-quantized values of theinput variables and setting variables of a single test case result in a(unique) value of the optimization function (g) or a derivative thereof,such as for example the figure of merit (f). For each value orcombination of the first dimension, the best result of the optimizationfunction within the second dimension is determined. A position of thedetermined result indicates the (quantized) combination of values ofsetting variables representing a best setting of the device under testfor each possible combination of input variables.

The filtering of the data set may be performed by a data set filter toobtain a best setting of a device under test for current input variablesusing an optimization vector. The optimization vector represents arelation between an index of input variables and an index of acombination of setting variables indicating the best setting of thedevice under test. Embodiments show that the data set filter is part ofthe test apparatus. Therefore, the optimization vector p may becalculated using the data processor or the test apparatus.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the present invention will be detailed subsequentlyreferring to the appended drawings, in which:

FIG. 1 shows a schematic block diagram of the test apparatus;

FIG. 2 shows an exemplary flow diagram showing an exemplary data set andthe respective tables of corresponding processing steps to derive thebest settings of the device under test;

FIG. 3 shows a schematic block diagram of a data set filter;

FIG. 4 shows a schematic block diagram of a method for performing a teston a device under test; and

FIG. 5 shows a schematic block diagram of a method for filtering a dataset using a data set filter.

DETAILED DESCRIPTION OF THE INVENTION

In the following, embodiments of the invention will be described infurther detail. Elements shown in the respective figures having the sameor a similar functionality will have associated therewith the samereference signs.

FIG. 1 shows a schematic block diagram of the test apparatus 2 forperforming a test on a device under test. The test apparatus 2 comprisesa data storage unit 4 and a data processor 6. The data storage unit isconfigured to store sets of input data 8 applied to the device undertest 3 (drawn with dashed lines since it is not part of the testapparatus) during the test and to store the respective output data 10 ofthe device under test. The output data 10 is obtained from the deviceunder test as a response of the device under test to the input data 8.The input data 8 comprises values of setting variables related tosettings of the device under test and values of input variablescomprising further information. Each set of input data represents onetest case. Moreover, the data processors 6 is configured to process thedata 12 stored in the data storage unit 4 such that a best combinationof setting variables of the device under test is determined for one ormore combinations of the input variables to obtain an optimized settingof the device under test for the one or more combinations of the inputvariables. The further information may comprise at least one of astimulus waveform, an input pattern, a stimulus signal, an environmentcondition, or an environment status.

FIG. 2 shows a schematic flow diagram with a sequence of exemplarytables 202, 204, 206, 208 representing results of selected steps whichmay be performed by the data processor to derive the best settings 11 ofthe device under test, e.g. represented by the parameter vector 16″. Thebest setting of the device under test may refer to a (optimal or best)combination of values of the setting variables for a current set ofvalues of input variables. The first table 202 shows a short extract ora short excerpt of the data storage unit 4 showing four different testcases 14. Moreover, the data storage unit comprises input variables x₁and x₂ 8 a and setting variables y₁ and y₂ 8 b. Output data of thedevice under test has been omitted for simplification.

In other words, given is a table (database, data storage unit) 4 with Ktest cases 14, k=1 . . . K with multiple variables.

The goal is to find the (best) values of Q variables y(k)=y₁(k), . . . ,y_(Q)(k) as a function of P variables x=x₁(k), . . . , x_(P)(k) thatoptimize variable values g(k). Optionally, test cases with the optimumcombination of x variables and y variables are selected.

Variables x_(p) and y_(q) can be real-valued, integer, Boolean,categorical, or of any other suitable data type.

The user can specify whether a good value of variable g is

-   -   a. small or    -   b. large or    -   c. deviates little from a desired value g₀ or    -   d. deviates a lot from an undesired value g₀.

The user can chose to find the

-   -   A. best worst case or    -   B. best average or    -   C. best (best) case

According to embodiments, the data processor 6 is configured to performa quantization of the input data 8 of at least one input variable 8 aand/or setting variable 8 b to form a discrete representation 8′ of theinput data. Therefore, the data processor may process the discreterepresentation 8′ of the input data 8 such that the best setting of thedevice under test 3 for the one or more combinations of the inputvariables 8 a is determined. This is advantageous, since it forms acountable or discrete number or amount of possible value combinations ofall input variables 8 a and/or setting variables 8 b.

In other words, if variables x_(p)(k) or y_(q)(k) take on too manyvalues in k=1 . . . K (e.g. when they are real valued or integer), theirvalues are quantized to N_(p) discrete values for variables x_(p) andM_(q) different discrete values for variables y_(q). For Boolean orcategorical variables, quantization is not necessary.

x _(p,1) ,x _(p,2) , . . . ,x _(p,N) _(p) for p=1 . . . P

y _(q,1) ,y _(q,2) , . . . ,y _(q,M) _(q) for q=1 . . . Q  (1)

Variables values x_(p)(k) and y_(q)(k) can now be represented asindices, 1≤n_(p)(k)≤N_(p), and 1≤m_(q)(k)≤M_(q).

n _(p)(k):x _(p)(k)=x _(p,n) _(p) _((k))

m _(q)(k):y _(q)(k)=y _(q,m) _(q) _((k))  (2)

To simplify notation, from now on, x_(p)(k) and y_(q)(k), refer to thequantized variable values 8′.

The second table 204 shows the storage unit with quantized values as aresult of the quantization of the first table 202 representing thestorage unit 4. The quantization to integer values is exemplary and maybe further applied to any other quantization pattern or quantizationsteps.

According to a further embodiment, all possible combinations of inputvariables 8 a and setting variables 8 b may be assigned or allocated toa (unique) placeholder or value or representative, such as for examplean integer value. In other words, after quantization, there are N=N₁·N₂·. . . ·N_(P) different combinations x_(n) of variable values. For eachtest k, the combination x(k) is described by an index n(k)=1 . . . N,such that

x _(n(k)) =x(k),  (3)

which can e.g. be computed as

$\begin{matrix}\begin{matrix}{{n(k)} = {1 + \left( {{n_{1}(k)} - 1} \right) + {\left( {{n_{2}(k)} - 1} \right)N_{1}} + {\left( {{n_{3}(k)} - 1} \right)N_{1}N_{2}} + \ldots}} \\{= {1 + {\sum\limits_{p = 1}^{P}\; {\left( {{n_{p}(k)} - 1} \right){\prod\limits_{i = 1}^{p - 1}\; N_{i}}}}}}\end{matrix} & (4)\end{matrix}$

For a given n, the reverse mapping is

$\begin{matrix}\begin{matrix}{n_{1} = {n\mspace{14mu} \% \mspace{14mu} N_{1}}} \\{n_{2} = {{{round}\left( \frac{n}{N_{1}} \right)}\mspace{14mu} \% \mspace{14mu} N_{2}}} \\{\cdots {~~~~~~~}} \\{n_{p} = {{{round}\left( \frac{n}{\Pi_{i}^{p - 1}N_{i}} \right)}\mspace{14mu} \% \mspace{14mu} {Np}}}\end{matrix} & (5)\end{matrix}$

where ‘%’ denotes the modulo operation.

Similar for variables y:

$\begin{matrix}{y_{m{(k)}} = {y(k)}} & (6) \\\begin{matrix}{{m(k)} = {1 + \left( {{m_{1}(k)} - 1} \right) + {\left( {{m_{2}(k)} - 1} \right)M_{1}} + {\left( {{m_{3}(k)} - 1} \right)M_{1}M_{2}} + \ldots}} \\{{= {1 + {\sum\limits_{p = 1}^{P}\; {\left( {{m_{p}(k)} - 1} \right){\prod\limits_{j = 1}^{p - 1}\; M_{j}}}}}},}\end{matrix} & (7) \\{m_{q} = {{{round}\left( \frac{m}{\Pi_{j}^{q - 1}M_{j}} \right)}\mspace{14mu} \% \mspace{14mu} M_{q}}} & (8)\end{matrix}$

In other words, the data processor 6 is configured to assign a uniquevalue to each possible combination of input variables and/or to eachpossible combination of setting variables. More specifically, the dataprocessor 6 is configured to assign the unique value to each possiblecombination of input variables 8 a using formula (4), where k is thecurrent test case, n(k) is the unique value of each possible combinationof input variables, n_(p)(k) is an index representing possible values ofthe input variable p, P is the total number of input variables p, andN_(i) is the total number of possible values of input variable i.Additionally or alternatively, the data processor 6 is configured toassign the unique value to each possible combination of settingvariables using formula (7) where k is the current test case, m(k) isthe unique value of each possible combination of setting variables,m_(p)(k) is an index representing possible values of the settingvariable p, P is the total number of setting variables p, and M_(j) isthe total number of possible values of setting variable j.

According to further embodiments, the data processor 6 may assignindices representing the test cases 14 to a respective combination ofinput variables 8 a of each possible combination of input variables andto a respective combination of setting variables 8 b of each possiblecombination of setting variables. The aforementioned assignment may beformed or applied in a table 206 such that a position along the firstdimension of the table indicates a unique value 18 a of a current inputvariable 8 a within all possible combinations of input variables and/ora position along the second dimension of the table 206 indicates aunique value 18 b of a current setting variable 8 b within all possiblecombinations of setting variables. This is advantageous, since each testcase representing the used non-quantized values, is directly related tothe quantized representation thereof and therefore, an output or aresult of the optimization value with non-quantized values may becalculated and assigned directly to the quantized representation and iseasily accessible by means of computer programming languages.

In other words, test cases for combinations of x and y shall be found.This may be applied by identifying the sets

(n,m) of test cases for all combinations n=1 . . . N of variables x andall combinations m=1 . . . M of variables y.

(n,m)={k:(x(k)=x _(n)){circumflex over ( )}(y(k)=y _(m))}  (9)

Note, this set (or each field) can contain no or one or multiple testcases or indices thereof, which causes no problem in the followingsteps. No test cases may be applied or derived, if the test does notcover all possible variable combinations, e.g. if the test is abortedbefore the test is completely performed. Multiple test cases may beapplied or derived due to the quantization of the variables of the testcases. This may be seen in the context of a possible applicationscenario.

For example, a device under test may be tested using the randomlycreated test cases. For large data sets, it is most likely that theduration of the test, started at the end of a working day, exceeds onenight or even a weekend and that therefore, the test is still running inthe morning of the next working day and therefore, results are notpresent. The same counts if the test is interrupted during processing,e.g. due to an error in the test routine or a power outage. Nonetheless,in contrast to known methods which fill a parameter space covering theresults of the test in an organized, sorted or regular manner, forexample one dimension after the other, the proposed device alreadycovers a huge variety of the parameter space. However, the coverage maybe not as dense as in the known approaches. This counts for thosedimensions, where the parameter space is already filled regularly by theknown approaches, wherein the coverage of those areas of the variablespace, which is not already regularly filled by the known approaches maymuch denser. However, according to the invention, it is possible todetermine dependencies or relations between all used variables of thetest cases at an early stage of the test. Using one of the knownapproaches, dependencies on one or more of the used variables may nothave been examined at the same test stage. Moreover, the proposed randomtest case generation provides the same results, only derived in adifferent order, than a deterministic approach, if the whole test isperformed. Nonetheless, a huge amount of tests is aborted or interruptedduring processing. In this case, the random test case generationoutperforms the deterministic approach since variables of the randomtest case generation are nevertheless varied or have a high variationwherein in a classical nested loop for example, the variable of theoutmost loop is varied comparably slowly.

Moreover, the data processor 6 may determine a best setting of thedevice under test 3 based on an optimization target 16, which may be afunction of output variables, wherein values of the output variablesform the output data 10. In other words, e.g. to form a quality metricsuch as the (processed) optimization target or figure of merit, theoptimization target may weight different output variables derived fromthe device under test 3 to receive a single function which can beoptimized. Based on the optimization target, the data processor maygenerate a standardized or processed optimization target, such that asmall value of the standardized optimization target refers to a goodsetting of the device under test. The data processor may thereforedetermine the best setting 11 of the device under test based on thestandardized optimization target. This is advantageous, since by usingthe standardized optimization target, the optimization targets areeasily exchangeable or replaceable by other possible standardizedoptimization targets, since a minimum of the chosen standardizedoptimization target is in all cases the best case. Otherwise, byreplacing a (non-standardized) optimization target, the best achievablevalue or limit of the optimization target is obviously also to bereplaced.

In other words, to simplify optimization, it is convenient to transformvariable g(k) into a figure of merit f(k) for each test that can beminimized, i.e. where small is good.

$\begin{matrix}{{f(k)} = \left\{ \begin{matrix}{+ {g(k)}} & {{Case}\mspace{14mu} {a.}} \\{- {g(k)}} & {{Case}\mspace{14mu} {b.}} \\{- {{{g(k)} - g_{0}}}} & {{Case}\mspace{14mu} {c.}} \\{{{g(k)} - g_{0}}} & {{Case}\mspace{14mu} {d.}}\end{matrix} \right.} & (10)\end{matrix}$

Letters a, b, c, d refer to the options for a good value of variable gdescribed above.

According to further embodiments, the data processor 6 may assign aresult of an optimization target 16 for each test case to a respectivecombination of input variables of each possible combination of inputvariables 8 a and to a respective combination of setting variables 8 bof each possible combination of setting variables, wherein, if multipleresults of test cases are assigned to the same combination of inputvariables and the same combination of output variables, a single value20 representing the multiple results is determined. The single value 20representing the multiple results of the optimization target may becalculated using the maximum value of the multiple results of theoptimization target, the maximum value of the multiple results of theoptimization target, or an average value of the multiple results of theoptimization target. Embodiments show that the assignment of the resultof the optimization target may be applied in a further table. This isadvantageous, since each result of the (standardized) optimizationtarget derived using e.g. the non-quantized values of a test case 14, isdirectly related to the respective combination of (quantized) values ofinput variables 8 a and the respective combination of (quantized)variables of setting variables 8 b. Therefore, an output or a result ofthe optimization value with non-quantized values may be calculated andassigned directly to the quantized representation and is easilyaccessible by means of computer programming languages.

Therefore, the data processor may assign, for each non-empty field of atable, where indices representing the test cases are stored, a result ofan optimization target based on the output values referring to the oneor more indices stored in the field to a corresponding field of afurther table, wherein, if multiple indices are related to one field, asingle value representing the multiple results of the optimizationtarget is assigned to the corresponding field of the further table. Inother words, the searched optimization function y(x) 16, is essentiallyalso a function of indices, m(n). Because there can be multiple testcases (or one or none)

(n,m) (cf. table 206) for each pair (n,m), the figure of merit is nowconsolidated to F(n,m) (cf. table 208) across all test cases in set

(n,m).

$\begin{matrix}{{f\left( {n,m} \right)} = \left\{ \begin{matrix} & {{Case}\mspace{14mu} {A.}} \\ & {{Case}\mspace{14mu} {B.}} \\ & {{Case}\mspace{14mu} {C.}}\end{matrix} \right.} & (11)\end{matrix}$

When

(n,m) is empty, F(n,m) is set to infinity so that it does not affectminimization. Letters A, B, C refer to the options described above.

According to further embodiments, the data processor may determine anindex of the best combination of setting variables for the one or morecombinations of input variables within at least one test case, whereinthe index indicates a unique value of the best combination of settingvariables within all possible combinations of values of settingvariables. This is advantageous, since similar values of input variables8 a may be quantized to the same representation 8′ thereof and, in thecontext of probably not having a complete test set, not tested with thesame combinations of values of input variables. However, it is mostlikely, that the combination of setting variables which performs bestfor one test case, performs best or at least near the optimum for allfurther test cases related to the same quantized values of inputvariables.

In other words, to find the optimum function y(x), for each combinationn=1 . . . N of variables x 8 a, the best combination μ(n) 16″ across allcombinations m=1 . . . M of variables y 8 b that minimize theconsolidated figure of merit F(n,m) shall be found. Note that a smallvalue of F(n,m) is good.

$\begin{matrix}{{\mu (n)} = {\underset{m = {1\mspace{14mu} \ldots \mspace{14mu} M}}{argmin}\left\{ {F\left( {n,m} \right)} \right\}}} & (12)\end{matrix}$

This defines the optimum combination μ(n) 16″ of indices for variablevalues y 8 b as a function of combination n of variable values x. Interms of variables x and y, the optimum function is

y _(μ(n))(x _(n))  (13)

To find the optimal test cases, the set

_(opt) of test cases that satisfy the optimality condition is given by

_(opt) ={k:m(k)=μ(n(k))}.  (14)

One way to show the effectiveness of optimization is to show thedistribution of g(k) for test cases in

_(opt) compared to all test cases.

Performing optimization, or calibration, is thus equivalent to applyinga data set filter based on an expression that is true, when

m(k)=μ(n(k))  (15)

Therefore, a data set filter for filtering a data set may obtain a bestsetting 11 of a device under test for current input variables using anoptimization vector 16″, the optimization vector representing a relationbetween an index 18 a of input variables 8 a and an index 18 b of acombination of setting variables 8 b indicating the best setting of thedevice under test.

A schematic representation of the data set filter 100 is shown in FIG.3. Therefore, input to the optimization vector may be values of inputvariables 8 a, e.g. from the data storage unit 4. Therefore, a currentlytested device under test, e.g. not the same but of the same type wherethe optimization vector was derived from, may be evaluated using theoptimization vector 16″. This is advantageous, since the finding of thebest variable combinations has been performed beforehand and the bestvalue combination may be obtained by only pointing on the correspondingfield in the data storage unit. Therefore, and according to furtherembodiments, the data set filter 100 may be implemented in the device(the earlier device under test now in use) such that the settingvariables are set, in real-time, meaning that the output of the deviceis obtained using the best value combination of setting variablesderived from the optimization vector, based on the currently appliedvalues of input variables. Moreover, the data set filter may be part ofthe test apparatus 2, for example, implemented in the data processor 6or derived from the data processor 6. However, further embodiments showthat the data processor 6 calculates the optimization vector μ(n) whichis used by the data set filter 100 to determine the best setting of thedevice under test. In other words, the test apparatus comprises the dataset filter, wherein the data processor is configured to form theoptimization vector.

According to embodiments applicable test cases for all combinations of xand y are extracted, where both have been quantized to obtain atractable number of combinations. Then, for each combination of x, thebest combination of y is determined that minimizes a consolidated (e.g.worst case) optimization metric across the applicable test cases.Overall this defines the optimal function ŷ(x). Performing optimizationor calibration translates to applying a filter that returns test caseswhere y(k)=ŷ(x(k)). To verify the effectiveness of optimization orcalibration, the behavior of those test cases that satisfy theoptimality criteria can be compared against all test cases, e.g. bycomparing their distributions of optimization target variable g(k).

FIG. 4 shows a flowchart of a method 300 for performing a test on adevice under test 3. The method 300 comprises a step 302 of storing setsof input data applied to the device under test during test, a step 304of storing the respective output data of the device under test derivedfrom the device under test as a response of the device under test to theinput data, wherein the input data comprises values of variables relatedto settings of the device under test and values of input variablescomprising known information, wherein each set of input data representsone test case, and a step 306 of processing the data stored in the datastorage unit such that the best setting of the device under test for oneor more combinations of the further variables is determined.

FIG. 5 shows a schematic flowchart of a method 400 for filtering a dataset to obtain a best setting of a device under test for current inputvariables using an optimization vector. The method 400 comprises a step402 of forming the optimization vector representing a relation betweenan index of input variables and an index of a combination of settingvariables indicating the best setting of the device under test.

Although the present invention has been described in the context ofblock diagrams where the blocks represent actual or logical hardwarecomponents, the present invention can also be implemented by acomputer-implemented method. In the latter case, the blocks representcorresponding method steps where these steps stand for thefunctionalities performed by corresponding logical or physical hardwareblocks.

Further embodiments of the invention relate to the following examples:

1. Optimization algorithm operating on a fixed subset of (x,y) pairs.Optionally, x and y are multiple variables, e.g. vectors. Furtheroptionally, x and y variables can be a mix of numeric and categoricaldata. Therefore, numeric variables may be quantized.2. Moreover, the calibration (of a device under test) may be implementedas a dataset filter.

Although some aspects have been described in the context of anapparatus, it is clear that these aspects also represent a descriptionof the corresponding method, where a block or device corresponds to amethod step or a feature of a method step. Analogously, aspectsdescribed in the context of a method step also represent a descriptionof a corresponding block or item or feature of a correspondingapparatus. Some or all of the method steps may be executed by (or using)a hardware apparatus, like for example, a microprocessor, a programmablecomputer or an electronic circuit. In some embodiments, some one or moreof the most important method steps may be executed by such an apparatus.

Depending on certain implementation requirements, embodiments of theinvention can be implemented in hardware or in software. Theimplementation can be performed using a digital storage medium, forexample a floppy disc, a DVD, a Blu-Ray, a CD, a ROM, a PROM, and EPROM,an EEPROM or a FLASH memory, having electronically readable controlsignals stored thereon, which cooperate (or are capable of cooperating)with a programmable computer system such that the respective method isperformed. Therefore, the digital storage medium may be computerreadable.

Some embodiments according to the invention comprise a data carrierhaving electronically readable control signals, which are capable ofcooperating with a programmable computer system, such that one of themethods described herein is performed.

Generally, embodiments of the present invention can be implemented as acomputer program product with a program code, the program code beingoperative for performing one of the methods when the computer programproduct runs on a computer. The program code may, for example, be storedon a machine readable carrier.

Other embodiments comprise the computer program for performing one ofthe methods described herein, stored on a machine readable carrier.

In other words, an embodiment of the inventive method is, therefore, acomputer program having a program code for performing one of the methodsdescribed herein, when the computer program runs on a computer.

A further embodiment of the inventive method is, therefore, a datacarrier (or a non-transitory storage medium such as a digital storagemedium, or a computer-readable medium) comprising, recorded thereon, thecomputer program for performing one of the methods described herein. Thedata carrier, the digital storage medium or the recorded medium aretypically tangible and/or non-transitory.

A further embodiment of the invention method is, therefore, a datastream or a sequence of signals representing the computer program forperforming one of the methods described herein. The data stream or thesequence of signals may, for example, be configured to be transferredvia a data communication connection, for example, via the internet.

A further embodiment comprises a processing means, for example, acomputer or a programmable logic device, configured to, or adapted to,perform one of the methods described herein.

A further embodiment comprises a computer having installed thereon thecomputer program for performing one of the methods described herein.

A further embodiment according to the invention comprises an apparatusor a system configured to transfer (for example, electronically oroptically) a computer program for performing one of the methodsdescribed herein to a receiver. The receiver may, for example, be acomputer, a mobile device, a memory device or the like. The apparatus orsystem may, for example, comprise a file server for transferring thecomputer program to the receiver.

In some embodiments, a programmable logic device (for example, a fieldprogrammable gate array) may be used to perform some or all of thefunctionalities of the methods described herein. In some embodiments, afield programmable gate array may cooperate with a microprocessor inorder to perform one of the methods described herein. Generally, themethods are performed by any hardware apparatus.

While this invention has been described in terms of several embodiments,there are alterations, permutations, and equivalents which fall withinthe scope of this invention. It should also be noted that there are manyalternative ways of implementing the methods and compositions of thepresent invention. It is therefore intended that the following appendedclaims be interpreted as including all such alterations, permutationsand equivalents as fall within the true spirit and scope of the presentinvention.

1. A test apparatus for performing a test on a device under test, thetest apparatus comprising: a data storage unit configured to store setsof input data applied to the device under test during the test and tostore output data of the device under test, wherein the output data isacquired from the device under test as a response of the device undertest to the input data, wherein the input data comprises values ofsetting variables related to settings of the device under test andvalues of input variables comprising further information, wherein eachset of input data represents one test case; and a data processorconfigured to: process data stored in the data storage unit; determine aselect combination of setting variables of a plurality of candidatecombinations of setting variables of the device under test for testingone or more combinations of the input variables; and acquire a selectedsetting of the device under test of a plurality of candidate settingsfor testing the one or more combinations of the input variables.
 2. Thetest apparatus according to claim 1, wherein the data processor isconfigured to perform a quantization of at least one input variable orat least one setting variable of the input data to form a discreterepresentation of the input data; and wherein the data processor isfurther configured to process the discrete representation of the inputdata to determine the selected setting of the device under test fortesting the one or more combinations of the input variables.
 3. The testapparatus according to claim 1, wherein the data processor is furtherconfigured to determine the selected setting of the device under testbased on a selection target, wherein the selection target is a functionof output variables, and further wherein values of the output variablesform the output data.
 4. The test apparatus according to claim 3,wherein the data processor is further configured to generate astandardized selection target based on the selection target, such thatwherein a small value of the standardized selection target is associatedwith a setting of the device under test, wherein the data processor isconfigured to determine the selected setting of the device under testbased on the standardized selection target.
 5. The test apparatusaccording to claim 1, wherein the data processor is further configuredto assign a unique value to each candidate combination of the inputvariables or to each candidate combination of the setting variables. 6.The test apparatus according to claim 5, wherein the data processorfurther is configured to assign the unique value to each candidatecombination of the input variables using the formula:n(k)=1+Σ_(p=1) ^(P)(n _(p)(k)−1)Π_(i=1) ^(p−1) N ₁ where k is a currenttest case, n(k) is a unique value of each candidate combination of theinput variables, n_(p)(k) is an index representing possible values of aninput variable p, P is a total number of input variables p, and N_(i) isa total number of possible values of input variable i; and wherein thedata processor is further configured to assign a unique value to eachcandidate combination of the setting variables using the formula:m(k)=1+Σ_(p=1) ^(P)(m _(p)(k)−1)Π_(f=1) ^(p−1) M _(j) where k is acurrent test case, m(k) is a unique value of each candidate combinationof the setting variables, m_(p)(k) is an index representing possiblevalues of a setting variable p, P is a total number of setting variablesp, and M_(j) is a total number of possible values of setting variable j.7. The test apparatus according to claim 1, wherein the data processoris further configured to assign indices representing the test cases to arespective combination of input variables of each possible combinationof input variables and to a respective combination of setting variablesof each possible combination of setting variables.
 8. The test apparatusaccording to claim 7, wherein the data processor is further configuredto form the assignment of indices in a table, wherein a position along afirst dimension of the table indicates a unique value of a current inputvariable within all possible combinations of input variables or whereinthe data processor is further configured to form the assignment ofindices in a table, wherein a position along a second dimension of thetable indicates a unique value of a current setting variable within allpossible combinations of setting variables.
 9. The test apparatusaccording to claim 1, wherein the data processor is further configuredto: assign a result of a selection target for each test case to arespective combination of input variables of each combination of inputvariables and to a respective combination of setting variables of eachcombination of setting variables; and responsive to a determination thatif multiple results of test cases are assigned to a same combination ofinput variables and a same combination of output variables, determine asingle value representing the multiple results.
 10. The test apparatusaccording to claim 9, wherein the data processor is further configuredto calculate the single value representing the multiple results of theselection target using one of: a minimum value of the multiple resultsof the optimization target, a maximum value of the multiple results ofthe optimization target, and an average value of the multiple results ofthe selection target.
 11. The test apparatus according to claim 1,wherein the data processor is further configured to determine an indexof a select combination of setting variables for the one or morecandidate combinations of input variables, wherein the index indicates aunique value of the select combination of setting variables within allpossible candidate combinations of values of setting variables.
 12. Thetest apparatus according to claim 1, wherein the further informationcomprises at least one of: a stimulus waveform, an input pattern, astimulus signal, an environment condition, and an environment status.13. The test apparatus according to claim 1, further comprising: a dataset filter for filtering a data set to acquire the selected setting ofthe device under test for the input variables using a selection vector,wherein the selection vector represents a relation between an index ofinput variables and an index of a combination of setting variables andindicates the selected setting of the device under test.
 14. Anapparatus comprising: a data set filter for filtering a data set toacquire a selected setting of a device under test for current inputvariables using a selection vector, wherein the selection vectorrepresents a relation between an index of input variables and an indexof a combination of setting variables and indicates the selected settingof the device under test, wherein the setting variables relate tosettings of the device under test and wherein the input variablescomprise at least one of a stimulus waveform, an input pattern, astimulus signal, an environment condition, or an environment status. 15.A method of performing a test on a device under test, the methodcomprising: storing sets of input data applied to the device under testduring the test; storing output data derived from the device under testas a response of the device under test to the input data, wherein theinput data comprises values of setting variables related to settings ofthe device under test and values of input variables comprising knowninformation, wherein each set of input data represents one test case;and processing the data stored in the data storage unit to determine aselect setting of the device under test for one or more combinations ofthe input variables.
 16. A method of filtering a data set, the methodcomprising: forming a selection vector, wherein the selection vectorrepresents a relation between an index of input variables and an indexof a combination of setting variables and indicates a select setting ofthe device under test; and acquiring a select setting of the deviceunder test for current input variables using the selection vector.
 17. Anon-transitory digital storage medium having a computer program storedthereon to perform the method of performing a test on a device undertest, the method comprising: storing in memory sets of input dataapplied to the device under test during the test; storing in memoryoutput data derived from the device under test as a response of thedevice under test to the input data, wherein the input data comprisesvalues of setting variables related to settings of the device under testand values of input variables comprising known information, wherein eachset of input data represents one test case; and processing the datastored in the data storage unit to determine a select setting of thedevice under test for one or more combinations of the input variables.18. A non-transitory digital storage medium having a computer programstored thereon to perform the method of filtering a data set, whereinthe method comprises: forming a selection vector, wherein the selectionvector represents a relation between an index of input variables and anindex of a combination of setting variables and indicates a selectsetting of the device under test; and acquiring a select setting of thedevice under test for current input variables using the selectionvector.
 19. The method of claim 15, wherein, in order to determine theselect setting of the device under test, the processing furthercomprises determining a select combination of the setting variables ofthe device under test for one or more combinations of the inputvariables.
 20. The method of claim 19, wherein the processing furthercomprises: performing a quantization of at least one input variable orat least one setting variable of the input data to form a discreterepresentation of the input data; and processing the discreterepresentation of the input data to determine the select setting of thedevice under test for the one or more combinations of the inputvariables.